Volume 14 | Issue 5 |

 Abstract

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Corporate governance, AI, Companies Act, stakeholder trust, ethical accountability, and OECD Principles

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Creative Commons Attribution 4.0 and The Open Definition

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The rapid growth of digital media and online social platforms has significantly increased the spread of fake news, which can mislead people and negatively impact public opinion, decision-making, and social harmony. This paper presents a content-based fake news detection system using a hybrid deep learning approach that combines Bidirectional Encoder Representations from Transformers (BERT) and Long Short-Term Memory (LSTM) networks to improve classification accuracy. BERT is utilized to generate contextualized word embeddings by understanding the semantic and syntactic relationships within the text, enabling the model to capture deep linguistic features from news content. The LSTM layer is integrated to effectively learn sequential patterns and long-term dependencies in the data, enhancing the model's ability to distinguish between real and fake information. The dataset used for this study consists of labeled news data from the FakeNewsNet repository, which is preprocessed using standard natural language processing techniques such as tokenization, stopword removal, text cleaning, and normalization. The model is trained and evaluated using performance metrics including accuracy, precision, recall, and F1-score, demonstrating improved results compared to traditional machine learning approaches. The system achieves an overall classification accuracy of approximately 88-90% on the test dataset and provides predictions in under two seconds, confirming its suitability for real-time applications. This work contributes to the development of intelligent systems for detecting misinformation and helps in reducing the harmful effects of fake news in online environments.

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Creative Commons Attribution 4.0 and The Open Definition

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Fake News Detection, BERT, LSTM, Transformer Models, Natural Language Processing, Deep Learning, Misinformation, Text Classification.

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Creative Commons Attribution 4.0 and The Open Definition

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The rapid proliferation of smartphones and AI technologies has created transformative opportunities in the domain of reservation and ticketing management. Traditional ticket booking systems suffer from long queue times, manual errors, overbooking, and lack of real-time updates. To address these challenges, an AI-Powered Reservation Ticket System with Mobile Integration has been developed. This system automates the entire booking lifecycle through intelligent slot allocation, dynamic pricing, real-time availability updates, and automated confirmation via mobile notifications. Key components include a machine learning-based demand forecasting engine, a mobile application (Android/iOS) for user interaction, a cloud backend for data management, and AI-driven chatbot support. The proposed system significantly reduces booking latency, improves resource utilization, and enhances user satisfaction. This paper outlines the system architecture, hardware and software components, implementation details, and evaluation results, along with future enhancements.

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Creative Commons Attribution 4.0 and The Open Definition

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Index Terms - AI Reservation System, Mobile Integration, Smart Ticketing, Machine Learning, Cloud Backend, Chatbot, Dynamic Pricing.

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Creative Commons Attribution 4.0 and The Open Definition

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Premenstrual Syndrome is recurrence of symptoms during the luteal phase of menstrual cycle. An exploratory study to assess knowledge regarding Premenstrual Syndrome among adolescent girls studying in selected nursing colleges of Indore. Quantitative research approach with one group Pretest Posttest research design was adopted & Non-probability purposive sampling technique was followed to select 60 adolescent girls. The self-structured questionnaire was administered. The study findings revealed that the majority of the adolescent girls, 48 (80%) were from the age group of 21-23 years, 71.7% had monthly income of family rupees above 15001, maximum 46.7% adolescents were vegetarians, 65% adolescent girls were menstruation menarche between 11-12 years of age, 48.3% adolescent girls had a history of PMS, maximum subjects 61.7% previous knowledge regarding PMS, 43.5% adolescents got the information from their friends and family members. The significant association were found between age of adolescent girls in which 21-23 years age of adolescent had average knowledge and showed a chi-square value (x2) = 6.336 was significant at 0.05 level as the p<0.5=6.336. So, it was concluded that premenstrual syndrome is characterized by the presence of both physical and behavioral symptoms that occur repetitively in the second half of the menstrual cycle and interfere with woman's life.

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Creative Commons Attribution 4.0 and The Open Definition

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Premenstrual Syndrome (PMS), Premenstrual Dysphoric Disorder (PMDD).

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Creative Commons Attribution 4.0 and The Open Definition

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Abstract: In the current business environment, the implementation of Big Data Analytics (BDA) becomes highly critical for innovating and improving performance. Thus, the aim of the paper is to examine the ways how organizations may process both structured and unstructured data to gain some useful insights and make more efficient business decisions. The growing amount of data created by social networking sites, Internet-based commercial transactions, and the Internet of Things makes the use of analytics highly crucial for businesses. Additionally, modern analytics allows firms to understand customer needs, optimize operations and launch new products or services thanks to sophisticated algorithms, which include machine learning and predictive analytics. Nevertheless, the paper will also address the need to use adequate methods for proper data processing and visualization. However, the usage of BDA faces several challenges related to data quality, data integration, expensive technology infrastructure, and scarcity of professionals

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Creative Commons Attribution 4.0 and The Open Definition

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Keywords: big data analytics, business innovation, data-driven decision-making, machine learning, predictive analytics, digital transformation.

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Creative Commons Attribution 4.0 and The Open Definition

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Pilonidal sinus is a chronic inflammatory condition predominantly affecting the sacrococcygeal region, characterized by a tract or cavity containing hair and debris. It is most common in young adult males and presents a therapeutic challenge due to its recurrent nature. In Ayurvedic medicine, this condition is correlated with Shalyajanya Nadi Vrana a subtype of sinus wound caused by a foreign body (Shalya), typically hair. This review synthesizes classical Ayurvedic concepts with contemporary surgical approaches, highlighting the pathogenesis, clinical features, and management strategies. Special emphasis is placed on Ayurvedic para-surgical techniques such as Kshara Karma and Kshara Sutra, which have demonstrated efficacy in reducing recurrence rates and promoting healing.

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Creative Commons Attribution 4.0 and The Open Definition

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Shalyaja Nadi Vrana, Pilonidal Sinus, Kshara Sutra, Ayurveda

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Creative Commons Attribution 4.0 and The Open Definition

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Bihar mein 1857 ki kranti daliton janjatiyon aur mahilaon ke yogdan ka aitihasik vishleshan

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Creative Commons Attribution 4.0 and The Open Definition

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Bihar mein 1857 ki kranti daliton janjatiyon aur mahilaon ke yogdan ka aitihasik vishleshan

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Creative Commons Attribution 4.0 and The Open Definition

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Mountain road safety in Indian Ghat sections is a major challenge due to hairpin bends, steep gradients, dense fog, and heavy monsoon rain, contributing heavily to road fatalities. With India recording 1.73 lakh road accident deaths in 2023 and 58.6% of fatal crashes linked to over-speeding at blind curves, this paper proposes a novel Multi-Criteria Decision Making framework to objectively compare five accident prevention systems for Ghat hairpin bends -- Smart Mirror, Sensor-Based Warning, Reflector-Based, AI-Based Alert, and IoT-Based Smart Warning -- using six criteria: Reliability, Effectiveness, Visibility Improvement, Cost, Maintenance, and Weather Adaptability. The study's key contributions include the first standardized MCDM comparison for Indian Ghats, scenario-based testing across low-fog, high-fog, heavy-rain, and night conditions, and a Salesforce-based web tool for real-time system selection. Validated across five Maharashtra Ghats -- Malshej, Amboli, Kasara, Tamhini, and Bhor -- the IoT-Based Smart Warning System performed best with a 4.70/6.0 score, 95% vehicle detection, and 90% weather adaptability, while the Sensor-Based Warning Signal was the most cost-effective active option at 73.3% of the max score. The paper recommends a three-layered deployment of IoT systems, active LED warnings, and passive delineators to improve safety.

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Ghat roads, hairpin bends, road safety, accident prevention, Multi-Criteria Decision Making, MCDM, IoT-Based Smart Warning System, Sensor-Based Warning Signal, vehicle detection, weather adaptability, Maharashtra, blind curves, Salesforce Experience Cloud, decision support tool

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

?-?????? ?? ??????????? ?????? ?? ???? ?? ?????? ?? ???? ??? ????????? ?? ???????? ?? ?????? ????, ???????? ??????? ?? ????????? ???? ?? ????? ?? ??? ???? ??? ???????????? ?????? (?-??????) ?? ???????? ??????? ?? ?? ??????? ?? ???? ??????-??-?????? (??2??) ??????? ???????? ?? ?????? ?????? ?? ?? ??? ??? ????????? ???? ???? COVID-19 ??????? ?? ?? ????? ?? ??? ?? ????, ?????-???, ??????-???, ??????-??? ?? ?-??? ??????? ?? ?????? ?? "?????? ????" ?????? ?? ??????? ????? ?? ???????????? ????????? (?-????/?????? ?????????) ?? ??????????? ????????? ??? ?????? ????? ?? ???? ?????? ?????? ???????, 1872, ????? ???????????? ???????, 2000 ?? ?????? ??????? ???????, 2023 (?????? ??????? ???????, 1872 ?? ????????????) ?? ????????, ?-???????? ?? ????????? ???? ???? ???? ?? ?????? ???????? ?? ?? ?????????? ?? ???? ???????? ???????? ???? ??? ?? ????? ?? ?? ???? ??????? ???? ????????, ????????, ?????, ?????, ??????, ?????, ??? ????? ?????? ?????? ??? ?????? ???? ???; ???????????? ??????? ?? ??????? ?????? ?? ???? ???? ??; ?? ???????????? ?? ????????? ???? ??? ????????? ???? ?? ?????? ?? ??????? ??, ????? ???? ????-??? ?? ??????, ?????? ???????? ?? ????? ?? ????-??? ??????????? ?? ????? ?? ????? ???? ???? ???????????? ???? ???????? (?????), ???????? ?????? ?? ??????????? ???????? ?? ?? ???? ???? ??, ?? ????? ???????, ???? ??? ????????, ???????? ?? ????-???? ??????? ?? ??????? ?????????? ?? ????? ?????? ?? ????? ???? ??? ?? ???????? ??????? ?? ?? ?????? ????? ???? ??? ?? ?-???????? ?? ??????? ???? ??, ????? ???????? ?? ??????? ????? ?? ????? ???? ??? ?????? ????????? ?????? ?? ??? ?????? ????? ?? ?????? ????? ??????????, ???? ??? ????????????? ?? ??? ?????? ????, ????? ????? ?????????? ?? ???????-??????? ?????? ?? ??? ???? ?

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

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???????????? ??????, ????? ???????????? ???????, ?????? ?????????, ?????? ???????? ???????, ????????? ???? ?

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Creative Commons Attribution 4.0 and The Open Definition

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The rapid advancement of artificial intelligence and deep learning technologies, the creation of highly realistic synthetic media, commonly known as deepfakes, has become increasingly accessible. While these techniques have promising applications in entertainment, education, and virtual reality, they also pose serious threats related to misinformation, identity theft, and digital fraud. This has created an urgent need for reliable and efficient deepfake detection systems.

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"Optimization and Evaluation of Blockchain-Based Authentication Systems with Zero-Knowledge Proofs and Multi- Factor Authentication"

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Creative Commons Attribution 4.0 and The Open Definition